1. Field of the Invention
The present invention relates to data transmission systems and, more particularly, to multicarrier data transmission systems.
2. Description of the Related Art
A conventional voice-band modem can connect computer users end-to-end through the Public Switched Telephone Network (PSTN). However, the transmission throughput of a voice-band modem is limited to below 40 Kbps due to the 3.5 KHz bandwidth enforced by bandpass filters and codes at the PSTN interface points. On the other hand, the twisted-pair telephone subscriber loop of a computer user has a much wider usable bandwidth. Depending on the length of the subscriber loop, the bandwidth at a loss of 50 dB can be as wide as 1 MHz. Transmission systems based on local subscriber loops are generally called Digital Subscriber Lines (DSL).
One DSL technique for high-speed data communications is Asymmetrical Digital Subscriber Line (ADSL) signaling for the telephone loop which has been defined by standards as a communication system specification that provides a low-rate data stream from the residence to the telephone company's central office (upstream), and a high-rate data stream from the central office to the residence (downstream). The ADSL standard provides for operation without affecting conventional voice telephone communications, e.g., Plain Old Telephone Service (POTS). The ADSL upstream channel only provides simple control functions or low-rate data transfers. The high-rate downstream channel provides a much higher throughput. This asymmetrical information flow is desirable for applications such as video-on-demand (VOD).
An ADSL modem operates in a frequency range that is higher than the voice-band; this permits higher data rates. However, the twisted-pair subscriber line has distortion and losses which increase with frequency and line length. Thus, the ADSL standard data rate is determined by a maximum achievable rate for a length of subscriber lines. The ADSL standard uses Discrete Multi-Tone (DMT) modulation with the DMT spectrum divided into two-hundred fifty-six 4.3125 kHz carrier bands and a quadrature amplitude modulation (QAM) type of constellation is used to load a variable number of bits onto each carrier band independently of the other carrier bands.
Besides ADSL, another DSL technique for high-speed data communications over twisted-pair phone lines is known as Very High Speed Digital Subscriber Lines (VDSL). VDSL is intended to facilitate transmission rates greater than that offered by ADSL. The multicarrier transmission schemes used with VDSL can be Discrete Multi-Tone (DMT) modulation, or some other modulation scheme such as Discrete Wavelet Multi-Tone (DWMT) modulation, Quadrature Amplitude Modulation (QAM), Carrierless Amplitude and Phase modulation (CAP), Quadrature Phase Shift Keying (QPSK), or vestigial sideband modulation.
Digital communication systems transmit data from a transmitter over a channel to a receiver. In order to support reliable, high performance operation, digital communication systems often need to estimate the impulse response of the channel. The channel can represent a channel from the transmitter to a far-end receiver or from the transmitter to a near-end receiver. The digital communication system can utilize the estimated channel impulse response for far-end or near-end noise cancellation schemes and far-end channel equalization.
Prior approaches to estimating a channel impulse response have been implemented in either the time domain or the frequency domain. In the case of time-domain channel estimation techniques, the estimated channel is convolved with the transmitted signal in an adaptive manner. However, such a solution produces only a single error signal that is used to update all taps of a finite impulse response filter that provides the estimated channel. This approach is complex and slow to converge.
FIG. 1 is a block diagram of a conventional time-domain channel taps estimation apparatus 100. In such scheme, each error signal (err_sig) contains the effect due to incorrect estimation of all M taps of the channel being estimated. The error signal is used to update the M taps for use by a Finite Impulse Response (FIR) filter which produces the channel estimate. Consequently, updates to the channel estimate based on each error signal require changes to all M taps. The error signal is therefore a poor metric for updating the estimated taps based on Least Means Square (LMS) algorithm. As a result, this training method often results in slow convergence. Although a Recursive Least Squares (RLS) algorithm helps decouple the error signal, such an approach often translates to increased hardware complexity. Moreover, to generate the response based on the estimated channel for calculating the error signal, the transmit signal needs to be convolved with the estimated channel. Each of the estimated output samples thus requires M multiplications between the M taps for the estimated channel and M samples of the input signals. This requires considerable processing time and computation power.
Frequency-domain channel identification approaches are more common. One approach requires transforming time-domain signals to frequency-domain tones, training frequency-domain taps for a FIR filter that provides the estimated channel, and then finally converting the frequency-domain taps back to the time-domain channel estimate. This approach allows each tap to be independently trained and adapted in the frequency domain. However, the disadvantages of this frequency-domain approach are that additional hardware for fast Fourier transforms and inverse fast Fourier transforms are required on the receiver side, and that the training signals utilized must span the entire frequency bandwidth. Unfortunately, in some implementations of digital communication systems, there are restrictions on usage of certain frequencies for the purpose of training and thus the entire frequency bandwidth is sometimes not permitted to be used.
FIG. 2 is a block diagram of a conventional frequency-domain channel taps estimation apparatus 200. The required time-domain taps are derived from the IFFT transformation of an estimated frequency-domain response. The frequency-domain response is estimated through an independent adaptive update for each of the frequency taps. With the frequency-domain based approach to estimate the time-domain taps, the training algorithm requires a correct frequency response estimation at every frequency index. Failure to estimate certain frequency-domain taps correctly would result in a poor time-domain response estimate. Since the frequency responses are correctly estimated only if a training signal at that frequency is excited, this requirement is a problem for a lot of standard frequency plans, e.g., ADSL or VDSL. In these frequency plans, the training signals are not allowed to transmit over the full spectrum. To deal with the problem, one approach uses extrapolation or interpolation of the correct frequency taps to the taps that could not be trained. However, extrapolation or interpolation fails to give reasonable performance, especially when a wide frequency band is not available for training, leading to a smaller number of correctly estimated taps.
Another approach to estimating the channel response in the frequency domain can utilize a frequency-domain adaptive comb filter. In K. Van Acker, M. Moonen, T. Pollet, “Per-Tone Echo Cancellation for DMT-based system,” IEEE Transactions on Communications, Vol. 51, No. 9 (September 2003), a per tone echo cancellation structure enables the transformation of time-domain taps to the frequency-domain adaptive comb filter taps. The frequency-domain adaptive comb filter taps can be directly trained to estimate the desired taps. The update of the adaptive comb filter taps for each of the frequency tones is unfortunately based on a single error signal, similar to the conventional time-domain channel taps estimation. As a result, this approach shares the same disadvantages of the conventional time-domain channel taps estimation, namely, high computational complexities and slow convergence.
Thus, there remains a need for improved approaches to estimating channel response in a digital communication system.